Decoding the information in the primary sequence of a protein is one of the most fundamental challenges in modern biology. A protein's sequence encodes more than just the native structure; it encodes the entire energy landscape - an ensemble of conformations whose energetics and dynamics are finely tuned. The goal of this proposal is a molecular, quantitative, and predictive understanding of the relationship between sequence and the energy landscape together with an understanding of how the environment modulates this landscape. A major hurdle in going from sequence to function is our lack of understanding of the non-native regions of the landscape. High-energy conformations are important for directing the stability and folding of a protein, and modulations of this ensemble play a role in misfolding, protein signaling, catalytic activity, and allostery. While many sequences can encode the same structure, their function and dynamics can vary dramatically. Small variations in a sequence can have effects that range from undetectable to pathological. These differences are often a consequence of subtle changes in the non-native regions of the landscape. Soon we will have access to thousands of human genomes, and without our ability to interpret variation, the potential of these data to impact medicine and human health will never be fully appreciated. It is imperative, therefore, that we have an understanding and control over the relationship between sequence and the energy landscape. Modulations of the energy landscape are not easily detected due to the small populations and transient nature of the high-energy species. The experiments outlined here are aimed at understanding how changes in the sequence and the environment affect the energy landscape. Aim 1: Determine how strain influences the energy landscape via single molecule mechanical studies. a. Determine how the geometry of pulling affects the transition state, or barrier for protein folding. b. Determine how the rate-limiting barrier to unfolding changes with force, and how these barriers relate to in vivo mechanical processes such as protein unfoldases. Aim 2: Probe the energy landscape through evolution and sequence modulation. a. Use Ancestral Sequence Reconstruction to probe evolutionary changes in the energy landscape. b. Evaluate the role of the hydrophobic core in protein function by selecting for core variants of DNA-binding proteins with altered binding specificities. Aim 3: Map unexplored regions of the energy landscape. a. Develop thiol exchange methods to map conformations on the folding reaction coordinate and utilize this kinetic partitioning method to characterize fluctuations on the native side of the transition state. b. Map out the sequence dependence of the earliest events in folding using both rapid mixing techniques and equilibrium models of early folding intermediates.